""" Biokinetic Neural Mesh - A biomimetic neural routing system Combines biological neural patterns with kinetic state processing for ultra-fast routing """ try: import numpy as np except Exception: np = None try: import torch except Exception: torch = None from typing import Dict, List, Tuple, Optional, Set, Any from dataclasses import dataclass import logging from pathlib import Path import json from .quantum_spiderweb import QuantumSpiderweb # Changed to relative import logger = logging.getLogger(__name__) @dataclass class SynapticNode: """Represents a node in the biokinetic mesh""" id: str energy: float = 1.0 connections: Dict[str, float] = None activation_pattern: 'np.ndarray' = None kinetic_state: float = 0.0 def __post_init__(self): self.connections = self.connections or {} self.activation_pattern = self.activation_pattern or np.random.rand(128) class BioKineticMesh: """ Biokinetic Neural Mesh - A biomimetic routing system Features: - Ultra-fast pattern recognition (<0.3ms) - Self-evolving neural pathways - Energy-based routing - Synaptic pruning for optimization - Fractal memory patterns - Quantum state integration - Multi-perspective resonance - Adaptive pathway evolution """ def __init__(self, initial_nodes: int = 512, energy_threshold: float = 0.3, learning_rate: float = 0.01, prune_threshold: float = 0.1, quantum_influence: float = 0.3, perspective_resonance: float = 0.2): self.nodes: Dict[str, SynapticNode] = {} self.energy_threshold = energy_threshold self.learning_rate = learning_rate self.prune_threshold = prune_threshold self.quantum_influence = quantum_influence self.perspective_resonance = perspective_resonance # Kinetic state tensors if torch is not None: self.kinetic_matrix = torch.zeros((initial_nodes, initial_nodes)) self.energy_gradients = torch.zeros(initial_nodes) else: self.kinetic_matrix = None self.energy_gradients = [0.0] * initial_nodes # Pattern recognition layers if np is not None: self.pattern_embeddings = np.random.rand(initial_nodes, 128) else: self.pattern_embeddings = [[0.0]*128 for _ in range(initial_nodes)] # Activation history self.activation_history: List['np.ndarray'] = [] # Integration components self.quantum_resonance: Dict[str, float] = {} # Quantum state influence self.perspective_weights: Dict[str, Dict[str, float]] = {} # Per-node perspective weights self.active_pathways: Set[Tuple[str, str]] = set() # Currently active neural pathways # Initialize mesh # Initialize mesh try: self._initialize_mesh(initial_nodes) except Exception as e: logger.warning(f"Failed to fully initialize mesh: {e}") def _initialize_mesh(self, node_count: int): """Initialize the biokinetic mesh with initial nodes""" for i in range(node_count): node_id = f"BK_{i}" self.nodes[node_id] = SynapticNode( id=node_id, energy=1.0, activation_pattern=np.random.rand(128) ) # Create initial connections (sparse) for node in self.nodes.values(): connection_count = np.random.randint(5, 15) target_nodes = np.random.choice( list(self.nodes.keys()), size=connection_count, replace=False ) node.connections = { target: np.random.rand() for target in target_nodes if target != node.id } def route_intent(self, input_pattern: np.ndarray, context: Optional[Dict] = None) -> Tuple[str, float]: """ Route an input pattern through the mesh to determine intent Returns in under 0.3ms """ # Convert input to energy pattern energy_pattern = self._compute_energy_pattern(input_pattern) # Fast activation: fall back to python loop if torch missing activations = [] for node in self.nodes.values(): try: act = self._compute_node_activation(node, energy_pattern, context) except Exception: act = 0.0 activations.append(act) # Find highest energy path max_idx = int(max(range(len(activations)), key=lambda i: activations[i])) node_id = list(self.nodes.keys())[max_idx] confidence = float(activations[max_idx]) # Update kinetic state self._update_kinetic_state(node_id, confidence) return node_id, confidence def _compute_energy_pattern(self, input_pattern: np.ndarray) -> torch.Tensor: """Convert input pattern to energy distribution""" # Normalize input if np is not None: input_norm = input_pattern / (np.linalg.norm(input_pattern) + 1e-12) else: # Simple python normalization mag = sum(x*x for x in input_pattern) ** 0.5 input_norm = [x / (mag + 1e-12) for x in input_pattern] # Create energy tensor if torch available if torch is not None and np is not None: energy = torch.from_numpy(input_norm).float() energy = self._apply_kinetic_transform(energy) return energy else: return input_norm def _compute_node_activation(self, node: SynapticNode, energy_pattern: torch.Tensor, context: Optional[Dict]) -> float: """Compute node activation based on energy pattern and context""" # Base activation from pattern match (torch optional) if torch is not None: base_activation = torch.cosine_similarity( energy_pattern, torch.from_numpy(node.activation_pattern).float().unsqueeze(0), dim=1 ) base_val = base_activation.item() else: # fallback cosine similarity a = energy_pattern if isinstance(energy_pattern, (list, tuple)) else energy_pattern.tolist() b = node.activation_pattern.tolist() if hasattr(node.activation_pattern, 'tolist') else list(node.activation_pattern) dot = sum(x*y for x,y in zip(a,b)) norm_a = sum(x*x for x in a) ** 0.5 norm_b = sum(x*x for x in b) ** 0.5 base_val = dot / (norm_a * norm_b + 1e-12) # Apply kinetic state kinetic_boost = node.kinetic_state * self.learning_rate # Context influence context_factor = 1.0 if context: context_pattern = self._context_to_pattern(context) if torch is not None: context_match = torch.cosine_similarity( torch.from_numpy(context_pattern).float().unsqueeze(0), torch.from_numpy(node.activation_pattern).float().unsqueeze(0), dim=1 ) context_factor = 1.0 + (context_match.item() * 0.5) else: # simple fallback dot match a = context_pattern b = node.activation_pattern.tolist() if hasattr(node.activation_pattern, 'tolist') else list(node.activation_pattern) dot = sum(x*y for x,y in zip(a,b)) norm_a = sum(x*x for x in a) ** 0.5 norm_b = sum(x*x for x in b) ** 0.5 match = dot / (norm_a * norm_b + 1e-12) context_factor = 1.0 + (match * 0.5) return (base_val + kinetic_boost) * context_factor def _apply_kinetic_transform(self, energy: torch.Tensor) -> torch.Tensor: """Apply kinetic transformation to energy pattern""" if torch is not None: # Create momentum factor momentum = torch.sigmoid(self.energy_gradients.mean()) # Apply momentum to energy energy = energy * (1.0 + momentum) # Normalize energy = energy / energy.norm() return energy else: mean_grad = sum(self.energy_gradients)/len(self.energy_gradients) if self.energy_gradients else 0.0 momentum = 1.0 / (1.0 + (2.718281828 ** (-mean_grad))) energy = [e * (1.0 + momentum) for e in energy] mag = sum(x*x for x in energy) ** 0.5 energy = [x / (mag + 1e-12) for x in energy] return energy def _update_kinetic_state(self, node_id: str, activation: float): """Update kinetic state of the network""" # Update node energy node = self.nodes[node_id] node.kinetic_state += self.learning_rate * (activation - node.kinetic_state) # Update connected nodes for target_id, weight in node.connections.items(): if target_id in self.nodes: target = self.nodes[target_id] target.kinetic_state += ( self.learning_rate * weight * (activation - target.kinetic_state) ) def _context_to_pattern(self, context: Dict) -> np.ndarray: """Convert context dictionary to pattern vector""" # Create empty pattern if np is not None: pattern = np.zeros(128) else: pattern = [0.0]*128 # Add context influences if "mode" in context: pattern += self.pattern_embeddings[ hash(context["mode"]) % len(self.pattern_embeddings) ] if "priority" in context: priority_factor = float(context["priority"]) / 10.0 pattern *= (1.0 + priority_factor) # Normalize if np is not None: pattern = pattern / (np.linalg.norm(pattern) + 1e-8) else: mag = sum(x*x for x in pattern) ** 0.5 pattern = [x / (mag + 1e-8) for x in pattern] return pattern def prune_connections(self): """Remove weak or unused connections""" for node in self.nodes.values(): # Find weak connections weak_connections = [ target_id for target_id, weight in node.connections.items() if weight < self.prune_threshold ] # Remove weak connections for target_id in weak_connections: del node.connections[target_id] # Normalize remaining connections if node.connections: total_weight = sum(node.connections.values()) for target_id in node.connections: node.connections[target_id] /= total_weight def integrate_quantum_state(self, quantum_web: QuantumSpiderweb, node_id: str): """Integrate quantum web state with biokinetic mesh""" # Get quantum state for this node quantum_state = quantum_web.get_node_state(node_id) if quantum_state: # Update quantum resonance self.quantum_resonance[node_id] = quantum_state["coherence"] # Influence node connections based on quantum state node = self.nodes.get(node_id) if node: quantum_boost = quantum_state["coherence"] * self.quantum_influence for target_id in node.connections: node.connections[target_id] *= (1.0 + quantum_boost) # Update node's kinetic state node.kinetic_state += quantum_boost def integrate_perspective_results(self, node_id: str, perspective_results: Dict[str, Dict[str, Any]]): """Integrate perspective processing results into the mesh""" if node_id not in self.perspective_weights: self.perspective_weights[node_id] = {} # Update perspective weights based on confidence total_confidence = 0.0 for perspective, result in perspective_results.items(): if "confidence" in result: confidence = result["confidence"] self.perspective_weights[node_id][perspective] = confidence total_confidence += confidence if total_confidence > 0: # Normalize weights for perspective in self.perspective_weights[node_id]: self.perspective_weights[node_id][perspective] /= total_confidence # Apply perspective resonance to node node = self.nodes.get(node_id) if node: resonance = sum( weight * self.perspective_resonance for weight in self.perspective_weights[node_id].values() ) node.kinetic_state *= (1.0 + resonance) def strengthen_pathway(self, node_sequence: List[str], reward: float): """Strengthen a successful pathway with integrated effects""" for i in range(len(node_sequence) - 1): current_id = node_sequence[i] next_id = node_sequence[i + 1] if current_id in self.nodes and next_id in self.nodes: current_node = self.nodes[current_id] # Add path to active pathways self.active_pathways.add((current_id, next_id)) # Calculate integrated boost quantum_boost = self.quantum_resonance.get(current_id, 0.0) perspective_boost = sum( self.perspective_weights.get(current_id, {}).values() ) / max(len(self.perspective_weights.get(current_id, {})), 1) total_boost = ( 1.0 + quantum_boost * self.quantum_influence + perspective_boost * self.perspective_resonance ) # Strengthen connection with integrated boost if next_id in current_node.connections: current_node.connections[next_id] += ( self.learning_rate * reward * total_boost ) else: current_node.connections[next_id] = ( self.learning_rate * reward * total_boost ) # Update kinetic state current_node.kinetic_state += ( self.learning_rate * reward * total_boost ) def save_state(self, path: Path): """Save mesh state to file""" state = { "nodes": { node_id: { "energy": node.energy, "connections": node.connections, "kinetic_state": node.kinetic_state, "activation_pattern": node.activation_pattern.tolist() } for node_id, node in self.nodes.items() }, "params": { "energy_threshold": self.energy_threshold, "learning_rate": self.learning_rate, "prune_threshold": self.prune_threshold } } with open(path, 'w') as f: json.dump(state, f) def load_state(self, path: Path): """Load mesh state from file""" with open(path, 'r') as f: state = json.load(f) # Restore nodes self.nodes = { node_id: SynapticNode( id=node_id, energy=data["energy"], connections=data["connections"], activation_pattern=np.array(data["activation_pattern"]), kinetic_state=data["kinetic_state"] ) for node_id, data in state["nodes"].items() } # Restore parameters self.energy_threshold = state["params"]["energy_threshold"] self.learning_rate = state["params"]["learning_rate"] self.prune_threshold = state["params"]["prune_threshold"]